6 Data Cleaning Tips Before Visualizations
Blog post from Hex
Data visualization can be significantly distorted by unclean data, which often manifests in charts with misleading representations, such as duplicate entries inflating totals or inconsistent units causing false anomalies. To address these issues, data teams can follow six essential cleaning steps: handle nulls and missing values deliberately, standardize formats and deduplicate data, triage outliers with a defensible rationale, utilize charts during the cleaning process, maintain reproducibility of cleaning procedures, and automate preventative measures. These practices are crucial for ensuring reliable and trustworthy visualizations, especially as data quality remains a primary concern for data teams and a barrier to AI adoption. Implementing these steps in an iterative cleaning process, supported by environments that integrate SQL, Python, and visualization tools, can enhance the accuracy and trustworthiness of data-driven insights.
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